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基于弯曲高斯过程组合方法的光伏出力预测研究
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A Research of Estimation of Solar Power Generation Based on Warped Gaussian Process
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    摘要:

    针对光伏发电功率受多种天气因素影响造成预测难度大的现状,提出了一种基于弯曲高斯过程的混合模型,可以实现一天内任意时刻的光伏出力的概率预测,获得置信区间预测值和点预测值.该算法先由多元自适应回归样条模型实现对多维输入变量的约减,同时得到待预测值的先验数据,然后利用模糊C均值算法按天气类型对训练集数据和测试集的先验数据进行聚类,得到相似样本,再利用弯曲高斯过程模型对测试集数据进行估计,最后利用Bagging算法实现对子混合模型的集成学习,得到待预测值的区间估计和点估计.仿真及试验结果验证了该混合模型的有效性和可靠性.与高斯过程估计和BP神经网络分位数估计相比,该混合模型精度更高,实用性更强.

    Abstract:

    Considering the situation that photovoltaicpower generation is affected by a variety of weather factors,a hybrid model was proposed based on warped Gaussian process to predict the power generation,where probability of photovoltaic power generation at any time in one day can be realized and prediction point and prediction interval can be obtained. Firstly,multivariate adaptive regression splines model was used to reduce multidimensional input variables,and to obtain the prior data of test. According to the type of weather,fuzzy C-means algorithm was then used to divide the training data and prior data of test,and to obtain the similar samples. The warped Gaussian process was also used to estimate the test data. Finally,bagging algorithm was used to realize the integrated study,and to obtain the prediction interval and prediction point. By the simulation and experimental results,the validity and reliability of this hybrid model was verified. The results show that the hybrid model improves both accuracy and practicability,compared with Gaussian process predictions and BP quantile regression neural network predictions.

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程泽,刘琦,张霞.基于弯曲高斯过程组合方法的光伏出力预测研究[J].湖南大学学报:自然科学版,2017,44(10):99~108

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  • 在线发布日期: 2017-10-30
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